Proteomic and metabolomic characterization of cardiac tissue in acute myocardial ischemia injury rats

Hua Bai; Ke Sun; Jia-Hong Wu; Ze-Hao Zhong; Sen-Lei Xu; Hong-Ru Zhang; Yi-Huang Gu; Sheng-Feng Lu

doi:10.1371/journal.pone.0231797

Abstract

The pathological process and mechanism of myocardial ischemia (MI) is very complicated, and remains unclear. An integrated proteomic-metabolomics analysis was applied to comprehensively understand the pathological changes and mechanism of MI. Male Sprague-Dawley rats were randomly divided into a mock surgery (MS) group and an MI group. The MI model was made by ligating the left anterior descending coronary artery, twenty-four hours after which, echocardiography was employed to assess left ventricular (LV) function variables. Blood samples and left ventricular tissues were collected for ELISA, metabolomics and proteomics analysis. The results showed that LV function, including ejection fraction (EF) and fractional shortening (FS), was significantly reduced and the level of cTnT in the serum increased after MI. iTRAQ proteomics showed that a total of 169 proteins were altered including 52 and 117 proteins with increased and decreased expression, respectively, which were mainly involved in the following activities: complement and coagulation cascades, tight junction, regulation of actin cytoskeleton, MAPK signaling pathway, endocytosis, NOD-like receptor signaling pathway, as well as phagosome coupled with vitamin digestion and absorption. Altered metabolomic profiling of this transition was mostly enriched in pathways including ABC transporters, glycerophospholipid metabolism, protein digestion and absorption and aminoacyl-tRNA biosynthesis. The integrated metabolomics and proteomics analysis indicated that myocardial injury after MI is closely related to several metabolic pathways, especially energy metabolism, amino acid metabolism, vascular smooth muscle contraction, gap junction and neuroactive ligand-receptor interaction. These findings may contribute to understanding the mechanism of MI and have implication for new therapeutic targets.

Citation: Bai H, Sun K, Wu J-H, Zhong Z-H, Xu S-L, Zhang H-R, et al. (2020) Proteomic and metabolomic characterization of cardiac tissue in acute myocardial ischemia injury rats. PLoS ONE 15(5): e0231797. https://doi.org/10.1371/journal.pone.0231797

Editor: Meijing Wang, Indiana University School of Medicine, UNITED STATES

Received: January 5, 2020; Accepted: March 31, 2020; Published: May 4, 2020

Copyright: © 2020 Bai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: We have uploaded the mass spectrometry proteomics data, and the data are available via ProteomeXchange with identifier PXD017765.

Funding: This work was supported by the following fundings:Prof. LSF, Natural Science Foundation of China (No. 81774210); Prof. GYH, Natural Science Foundation of China (No.81674063, 81974583) and Jiangsu Provincial Leading Talents in Chinese Medicine (No.SLJ0226); Dr. ZHR, Natural Science Foundation of China (No.81704169) and the Natural Science Foundation of Jiangsu Province (No.BK20171067). All funders were participated the study, specifically, Hong-Ru Zhang revised the paper, Yi-Huang Gu and Sheng-Feng Lu designed the study. and revised the paper.

Competing interests: The authors declare that they have no conflict of interest.

Introduction

MI is a serious cardiovascular disease with great morbidity and mortality. The pathological process and mechanism of MI is very complicated; researchers have shown that microvascular dysfunction [1], inflammation [2] and cardiac fibrosis [3] are involved in MI, but the entire process remains unclear. Determining the pathological changes resulting from MI is necessary for the treatment and prognosis of MI. However, most researchers have been limited to exploring single pathways and ignored the crosstalk between each pathway, thus they could not achieve a comprehensive elucidation of the mechanism of MI. It is necessary to develop a more systematic method of investigation.

Systems biology emphasizes the integration of the different components of the human body, including genes, proteins and metabolites, and investigates potential correlations between multiple molecular levels. Omics technology, including genomics, transcriptomics, proteomics and metabolomics, is an important part of systems biology. In the past few decades, there have been remarkable achievements in the single omics study of MI [4, 5], but the exact mechanisms of MI are still unclear due to complex biochemical regulation at multiple levels. To reveal the process of MI in a more systematic way, the research of multiomics came into being, to promote the transformation of the MI research paradigm from a single parameter model to a multi parameter system model, and to potentially help with the understanding of the entire biological mechanism [6]. Among the omic study methodologies, proteomics and metabolomics reveal changes in proteins and metabolites respectively, and which in turn reflect the biological activities either ongoing or past [7]. Integrated proteomic-metabolomics is a powerful combination [8] and provides a better understanding of dynamic molecular change. Some studies have reported the changes of metabolites and proteins in plasma or myocardium after MI [9–11]; however, the integrated proteomic-metabolomics in myocardium after an MI has not been reported. Thus, we designed an integrated proteomic-metabolomics study, to comprehensively understand the pathological changes and the mechanisms of MI.

Methods

Experimental animals and grouping

A total of 20 male Sprague Dawley rats (2 months old, 250±20g) were purchased from Charles River Labs. After one week of adaptive feeding, the rats were acclimatized to a 12-hour light/ dark cycle in a controlled environment with a temperature of approximately 25°C and a relative humidity of 50%. They had free access to standard mouse chow and tap water. The rats were randomly divided into an MS group and an MI group. This study was approved by the Institutional Animal Care and Use Committee of Nanjing University of Chinese Medicine, and was conducted in accordance to the guidelines of the National Institutes of Health Animal Care and Use Committee.

MI model establishment

The MI model was established as previously described [11, 12]. The rats were anesthetized with 4% isoflurane (RWD, China) at an air flow rate of 0.6–0.8 L, and then kept anesthetized with 1%-2% isoflurane. They were then placed supine on a temperature- controlled experimental board set at 37 ±3°C and intubated with a small animal ventilator (RWD407, China) set at a respiratory rate of 60–70 breaths per minute. After disinfecting the surgical area, the left chest was opened to expose the heart between the 3^rd and 4^th intercostal space. The pericardium was separated, the heart was exteriorized, and the LAD was quickly ligated using 6.0 prolene suture at approximately 1 mm distal to the left atrial appendage and 2 mm in width and depth to induce MI. The appearance of a more pale color below the ligation area and ST-segment elevation on ECG (PowerLab System, AD Instruments, USA) were used to confirm a successful occlusion of the LAD. The chest cavity was then closed, and the rats were kept warm and allowed to recover. Approximately 400,000 units of penicillin was administered via intramuscular injection to prevent infection following the operation. In the MS group, the same procedure was performed except for the LAD ligation.

Echocardiography

Twenty-four hours after MI or mock surgery, left ventricular (LV) function variables were assessed by transthoracic echocardiography. After induction of anesthesia with 2% isoflurane, the rats underwent transthoracic two-dimensional (2D)-guided M-mode echocardiography with small animal ultrasound (Esaote, Italy). The rats were inclined 30° to the left and the ultrasonic coupling agent was applied. The ultrasonic probe was placed on the left side of the sternum, 10° ~ 30° from the median sternum, showing the long axis of the left ventricle. Using the image of the cardiac long axis, the ejection fraction (EF) and fractional shortening (FS) were calculated. FS was calculated as follows: FS = (left ventricular internal diameter at end-diastole (LVIDd)- left ventricular internal diameter at end-systole (LVIDs)) / LVIDd×100%; EF = (left ventricular end-diastolic volume (LV Vol; d)–left ventricular end-systolic volume (LV Vol; s)) / (LV Vol; d)×100%. Echocardiographic measurements were reported as the average of three separate cardiac cycles. All images were acquired at heart rates 350–420 bpm [13].

Collection of tissue and serum samples

Twenty-four hours after MI or mock surgery, the rats were sacrificed with a lethal dose of pentobarbitone sodium (100 mg/kg, C004801, Huamaike Bio, China)[14]. For 10 rats of each group, blood samples and left ventricular tissue samples from below the ligation were collected for subsequent analysis. Blood specimens were collected from the abdominal aorta and centrifuged for 10 minutes at 3500 rpm to obtain the serum. The serum was then stored at -80°C for cTnT and CK-MB determination. Left ventricular tissues were stored at -80°C for metabolomics and proteomics analysis.

Determination of serum level of cTnT

The levels of cTnT was assessed using ELISA kits (catalogue number: JTE69119; JinTing Bio, China) following the manufacturer’s protocol.

Proteomics analysis

Three biological replicates were prepared of each group for iTRAQ proteomics. The details of this process are described as follows:

Protein digestion was performed according to the Filter aided proteome preparation (FASP) procedure as described by Wisniewski et al. [15] and the resulting peptide mixture was labeled using iTRAQ reagent according to the manufacturer’s instructions (Applied Biosystems). Briefly, 200 μg of proteins for each sample was incorporated into 30 μl SDT buffers (4% SDS, 1 mM DTT, 100 mM Tris-HCl, pH 7.6). The detergent, DTT and other low-molecular-weight components were removed using a UA buffer (8 M Urea, 150 mM Tris-HCl pH 8.0) via repeated ultrafiltration (Microcon units, 30 kD). Next, 100 μl 0.05 M iodoacetamide in UA buffer was added to block reduced cysteine residues and the samples were incubated for 20 min in darkness. The filters were washed with 100 μl UA buffer three times, after which they were rinsed with 100 μl DS buffer (50 mM triethylammoniumbicarbonate at pH 8.5) twice. Finally, the protein suspensions were digested with 2 μg trypsin (Promega) in 40 μl DS buffer overnight at 37°C, and the resulting peptides were collected as a filtrate. The peptide content was estimated by UV light spectral density at 280 nm using an extinctions coefficient of 1.1 of 0.1% (g/l) solution that was calculated on the basis of the frequency of tryptophan and tyrosine in vertebrate proteins. For labeling, each iTRAQ reagent was dissolved in 70 μl of ethanol and added to the respective peptide mixture (100ug peptide of each sample). The samples were labeled as (Sample1)-1, (Sample2)-2, (Sample3)-3.

iTRAQ labeled peptides were fractionated by SCX chromatography using the AKTA Purifier 100 (GE Healthcare). The dried peptide mixture was reconstituted and acidified with 2 ml buffer A (10 mM KH₂PO4 in 25% of ACN, pH 3.0) and loaded onto a PolySULFOETHYL 4.6 x 100 mm column (5 μm, 200 Å, PolyLC Inc, Maryland, U.S.A.). The peptides were eluted at a flow rate of 1 ml/min with a gradient of 0%-10% buffer B (500 mM KCl, 10 mM KH2PO4 in 25% of ACN, pH 3.0) for 25 min, 10–20% buffer B for 7 min, 20%-45% buffer B for 10 min, 45%–100% buffer B for 5 min, and 100% buffer B for 13 min. The elution was monitored by absorbance at 214 nm, and fractions were collected every 1 min. The collected fractions (about 30 fractions) were finally desalted on C18 Cartridges (Empore™ SPE Cartridges C18 (standard density), bed I.D. 7 mm, volume 3 ml, Sigma).

Experiments were performed on a Q Exactive mass spectrometer that was coupled to Easy nLC (Proxeon Biosystems, now Thermo Fisher Scientific). 10 μl of each fraction was injected for nanoLC-MS/MS analysis. The peptide mixture (5 μg) was loaded onto a C18-reversed phase column (Thermo Scientific Easy Column, 10 cm long, 75 μm inner diameter, 3μm resin) in buffer A (0.1% Formic acid) and separated with a linear gradient of buffer B (84% acetonitrile and 0.1% Formic acid) at a flow rate of 300 nl/min. The mass spectrometer was operated in positive ion mode. MS data was acquired using a data-dependent top10 method dynamically choosing the most abundant precursor ions from the survey scan (300–1800 m/z) for HCD fragmentation. Determination of the target value was based on predictive Automatic Gain Control (pAGC). Dynamic exclusion duration was 60 s. Survey scans were acquired at a resolution of 70,000 at m/z 200 and resolution for HCD spectra was set to 17,500 at m/z 200. Normalized collision energy was 30 eV and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%.

MS/MS spectra were searched using a MASCOT engine (Matrix Science, London, UK; version 2.2) embedded into Proteome Discoverer 1.4 (Thermo Electron, San Jose, CA.) against Uniprot Rat database (36094 sequences, download at June 6th, 2018) and the decoy database. For protein identification, the following options were used: Peptide mass tolerance = 20 ppm, MS/MS tolerance = 0.1 Da, Enzyme = Trypsin, Missed cleavage = 2, Fixed modification: Carbamidomethyl (C), iTRAQ4/8plex(K), iTRAQ4/8plex(N-term), Variable modification: Oxidation(M), FDR≤0.01.

Metabolomics analysis

Quality control (QC) samples were prepared by retrieving equal number of samples from each group; of these, 10 samples from each group and QC samples were respectively analyzed, using the following process: 1mL of cold methanol/acetonitrile/H₂O (2:2:1,v/v/v) was added to 100mg of each sample, add and adequately vortexed for 30s. The lysate was homogenized by MP homogenizer (24×2, 6.0M/S, 60s, twice). The homogenate was sonicated at low temperature (30min/once, twice), and then incubated at -20°C for 1 h for protein precipitation. The mixture was centrifuged for 15 min (14000 rpm, 4°C). The supernatant was dried in a vacuum centrifuge and stored at -80°C. For LC-MS analysis, the samples were re-dissolved in 100 μL acetonitrile/water (1:1, v/v) solvent.

LC-MS/MS analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein Technology Co., Ltd. For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 μm column (waters, Ireland). In both ESI positive and negative modes, the mobile phase contained A = 25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B = acetonitrile. The gradient was 85% B for 1 min, and processed as follows: 1) linearly reduced to 65% in 11 min, 2) reduced to 40% in 0.1 min and kept for 4 min, and 3) increased to 85% in 0.1 min, with a 5 min re-equilibration period employed.

The ESI source conditions were set as follows: Ion Source Gas1 (Gas 1) as 60, Ion Source Gas (GAS 2) as 60, curtain gas (CUR) as 30, source temperature: 600°C, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60–1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25–1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan was acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V with ± 15 eV; declustering potential (DP), 60 V (+) and −60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10.

The raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing into freely available XCMS software. For peak picking, the following parameters were used: centWave m/z = 25 ppm, peakwidth = c (10, 60), prefilter = c (10, 100). For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. In the extracted ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept. Compound identification of metabolites by MS/MS spectra with an in-house database established with available authentic standards. After being normalized to total peak intensity, the processed data were uploaded into SIMCA-P (version 14.1, Umetrics, Umea, Sweden), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). A 7-fold cross-validation and response permutation testing methodology was used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Metabolites with the VIP value >1 was further applied to Student’s t-test at univariate level to measure the significance of each metabolite. In all analyses, p values <0.05 were considered as statistically significant.

Integrated analysis of proteins and metabolites

The Venny 2.1.0 online website was used to select the metabolic pathways in which both differentially expressed proteins and metabolites were involved in by. To intuitively and comprehensively integrate the pathway data, we then projected the differentially expressed proteins and metabolites into the KEGG pathway simultaneously.

Statistical analysis

Statistical analyses were completed using IBM SPSS 21.0. The data were presented as mean±standard errors of the mean. The normality of data distribution was tested with the Shapiro-Wilk test. If the data were normally distributed, groups were compared using an Independent-Samples T test. If the data were not normally distributed, groups were compared by using aMann-Whitney U test. A p value < 0.05 was considered statistically significant.

Results

Change in left ventricular function and myocardial enzyme level after MI

The left ventricular function was assessed by echocardiography. After MI, the motion of the anterior wall of left ventricle was attenuated (Fig 1A), and the EF and FS were also significantly decreased (Fig 1B and 1C). ELISA was performed to test the level of cTnT. The level of cTnT was significantly higher for the MI group compared to the MS group (Fig 1D).

Download:

Fig 1. The change of left ventricular function and level of cTnT after MI (n = 10 each group).

(a) Representative recording obtained from rats of MS and MI group at different timepoints. (b) FS of rats with MS and MI at different timepoints. (c) EF of rats with MS and MI at different timepoints. (d) Level of cTnT in the serum of rats MS and MI. All data are expressed as mean ± SD. ***P<0.001 vs MS group. MS: mock surgery, MI: myocardial ischemia.

https://doi.org/10.1371/journal.pone.0231797.g001

Myocardial protein identification and quantification comparisons after MI

The myocardial proteins were identified and quantified using LC-MS, software MASCOT 2.2 and Proteome Discoverer 1.4. With a highly conservative threshold (peptide FDR≤1%, the protein ratios are calculated as the median of only unique peptides of the protein), 3891 proteins with 20825 unique peptides were identified. A high-quality Q Exactive mass spectrometer was used for maintaining good quality deviation during the process of data acquisition and producing high-quality MS1 and MS2 spectrograms. The rigorous MASCOT analytical tool was used to judge each MS2 spectrogram. We obtained an ideal score with a median of 41.55, and more than 90.79% of the peptides scored higher than 20 (S1 Fig). FDR<0.01 was used as the screening standard in the qualitative analysis of iTRAQ data. The protein ratio (approximately 1.0) distribution of the two groups appears in S2 Fig In brief, analyzing the quality control data indicated that the identification results were accurate and reliable. A total of 169 proteins were altered including 52 proteins with increased expression and 117 proteins with decreased expression (Fig 2A and Table 1). Significant differentially expressed proteins were ranked using unsupervised hierarchical clustering (Fig 2B), which indicated the rationality and credibility of these models for investigating the differential proteins between the groups.

Download:

Fig 2. Proteomics analysis after MI.

(a) Volcano Plot for differential protein screening. The differentially expressed proteins were screened using the standard that the fold change be >1.2 or <0.83 and the p value <0.05. Red dots indicate the proteins with significantly differentially expressed proteins; black dots indicate the proteins with no differences. (b) Heat-map of differential proteins between MS and MI groups. (c) Gene Ontology enrichment for the differentially expressed proteins using Fisher’ Exact Test. The abscissa represents the GO functional classification; the ordinate represents the number of differentially expressed proteins in each functional classification. (d) KEGG pathway enrichment of the differentially expressed proteins. The ordinate represents the number of proteins, and the abscissa represents the significantly enriched KEGG pathways. The label at the top of the bar shows the number of differentially expressed proteins contained in each KEGG pathway. MS: mock surgery, MI: myocardial ischemia.

https://doi.org/10.1371/journal.pone.0231797.g002

Download:

Table 1. Levels of significantly changed proteins.

https://doi.org/10.1371/journal.pone.0231797.t001

Functional classification and annotation of differentially expresses proteins

To further explore which functional or biological pathways are significantly affected by MI, GO annotations were obtained using Blast2Go software. The differential proteins were annotated through three independent ontology in the GO database including the biological process, their molecular function and the cellular component. Fisher's Exact Test was then used to compare the distribution of each GO classification in the target protein set and to perform the enrichment analysis of the GO annotation of the target protein sets. The top 20 GO terms of the GO enrichment analysis appear in Fig 2C. The color of the bar chart reflects the significance of GO function classification based on Fisher's Exact Test. The color changes gradually from orange to red. The closer a color is to red, the smaller the p value, and the greater the significance of the corresponding GO function category enrichment. The label at the top of the bar shows the enrichment factor (richFactor≤1), which represents the proportion of the number of differentially expressed proteins to all identified proteins annotated with the GO functional category. Several top-ranking GO terms such as protein activation cascade and discoidal high-density lipoprotein particle are related to energy metabolism. The top-ranking enriched terms from molecular function were associated with amino acid, including peptidase inhibitor activity, endopeptidase inhibitor activity, endopeptidase regulator activity. In addition, we observed an altered extracellular region and MHC class I protein complex which are related to myocardial contraction.

KEGG pathway analysis for differentially expressed proteins identified after MI

The identified differential proteins in the MS and MI groups were further examined via the Kyoto Encyclopedia of Genes and Genomes (KEGG). Fisher's Exact Test was used to compare the distribution of each KEGG pathway in the target protein set and the total protein set, and to analyze the KEGG pathway enrichment of the target protein sets (Fig 2D). The results indicated that the metabolic pathways were significantly changed, including those involving the complement and coagulation cascades, tight junction, regulation of actin cytoskeleton, MAPK signaling pathway, endocytosis, NOD-like receptor signaling pathway, and phagosome coupled with vitamin digestion and absorption.

Metabolic profiles alterations after MI

To further investigate the effects of MI, metabolite profiles both before and after MI were obtained using LC-MS. As shown in S3 Fig, the total ion chromatograms of metabolites were detected. Accordingly, the relative intensity of and the peaks under positive mode (S3A Fig) and negative mode (S3B Fig) were different. Nevertheless, chromatograms were anastomotic between QCs in both ion modes, indicating that variation remained in the optimal range. The metabolite ion peaks were extracted by using XCMS software. A total of 8176 peaks were identified. After data normalization, PCA was used to determine the correlation between the two groups (Fig 3A). Next, OPLS-DA was used to highlight the differences. The results indicated that there were differential metabolic profiles between the two groups (Fig 3B). The reliability of a PLS-DA model was determined by using a permutation test (n = 200) and avoiding over-fitting (Fig 3C). These results indicated that the stability and repeatability of these models were sufficient to use them to investigate the differential metabolites between the groups. A total of 68 metabolites were differentially expressed (VIP>1 and p<0.05, Table 2). Hierarchical Clustering showed that the samples from the two groups appeared in different clusters (Fig 3D), indicating that the screening of differential metabolites was reasonable. We subsequently enriched these altered metabolites based on the KEGG pathway database (Fig 3E). The color of the bar chart reflects the significance of KEGG pathway based on Fisher's Exact Test. The color changes gradually from orange to red. The closer the color is to red, the smaller the p value, and the greater the significance of the corresponding KEGG pathway enrichment. The result showed that pathways were perturbed, mainly including ABC transporters, glycerophospholipid metabolism, protein digestion and absorption and aminoacyl-tRNA biosynthesis.

Download:

Fig 3. Metabolic alterations after MI.

(a) PCA analysis of MS and MI groups. (b) OPLS-DA score plots for the MS and MI groups. (c) Permutation test (n = 200) using PLS-DA model. (d) Heat-map of selected metabolites. (e) Enrichment analysis of common differential metabolites in MS and MI groups. The ordinate represents the significantly enriched KEGG pathway; the abscissa represents the number of differentially expressed metabolites contained in each KEGG pathway. The label at the right of the bar shows the enrichment factor (richFactor≤1), which represents the ratio of the differentially expressed proteins to all identified proteins involved in one KEGG pathway. MS: mock surgery, MI: myocardial ischemia.

https://doi.org/10.1371/journal.pone.0231797.g003

Download:

Table 2. List of differentially expressed metabolites.

https://doi.org/10.1371/journal.pone.0231797.t002

Integrated analysis of proteins and metabolites that were altered after MI

To associate the results of our proteomics and metabolomics analyses, we chose KEGG pathways as the carrier and conducted a mapping analysis based on the changed proteins and metabolites. A Venn diagram showed that there were 30 metabolic pathways in which both differentially expressed proteins and metabolites were involved (Fig 4), including neuroactive ligand-receptor interaction, ABC transporters, glycerolipid metabolism, pentose and glucuronate interconversions, fatty acid degradation, pyruvate metabolism, oxytocin signaling pathway, vascular smooth muscle contraction, gap junction, VEGF signaling pathway and inflammatory mediator regulation of TRP channels (S1 Table).

Download:

Fig 4. Venn diagram of the pathways in which differentially expressed proteins and metabolites were involved.

https://doi.org/10.1371/journal.pone.0231797.g004

Discussion

This study aimed at exploring the pathological results of an MI through proteomic-metabolomic analysis. The motion of the anterior wall of the left ventricle was attenuated, the EF and the FS were significantly decreased after MI, and the level of cTnT increased after MI, suggesting that our model of an MI was successful. We then explored the proteomic and metabolomic characterization of myocardium after MI. Integrated analysis of proteins and metabolites showed that multiple process significantly changed after MI, including those related to energy metabolism (e.g., fatty acid degradation, glycerolipid metabolism, glycolysis/gluconeogenesis), amino acid metabolism (e.g., glutamate, arginine, proline, histidine, lysine metabolism), others, such as ABC transporters, vascular smooth muscle contraction, gap junction, and neuroactive ligand-receptor interaction.

In response to energy starvation, cardiomyocytes undergo a series of metabolic changes, including the perturbations of circulating energy substrates. In our study, we found several perturbed pathways including fatty acid degradation, glycerolipid metabolism, and glycolysis/gluconeogenesis. Researchers have found that in the normal heart, fatty acids are mostly responsible for providing energy to keep the heart functioning (about 70% of cardiac ATP generation), and the other 30% of cardiac ATP synthesis is derived from glucose utilization [16]. However, after an MI, both fatty acid usage and glucose oxidation are inadequate, resulting in decreased ATP production [17] and mitochondrial dysfunction. A previous study has reported that lipid metabolites were the most significantly changed molecules in ST-segment elevation myocardial infarction (STEMI) patients who had undergone primary percutaneous coronary interventions (PPCI) [18]. While in our study, we observed decreased myo-inositol showing that alterations in lipid metabolism occurred in our rat MI model as previously reported [18, 19], we also observed a decrease in hydroxysteroid 17-beta dehydrogenase 10 (Hsd17b10), a mitochondrial protein that catalyzes the oxidation of a wide variety of fatty acids and steroids and is a subunit of mitochondrial ribonuclease P [20], which further verified that fatty acid oxidation inhibition and mitochondria impairment. The level of many glucose metabolites (e.g., glycerol 3-phosphate, dihydroxyacetone phosphate and glyceric acid) decreased, but the level of phosphoenolpyruvate increased, indicating that glycolysis was accelerated as reported in the literature [17, 21]. The malate-aspartate shuttle (MAS) has an important role in regulating glycolysis by transferring NADH+H⁺ from cytosol into mitochondria. Our team has previously reported that the MI group had significantly lower levels of MAS metabolites (e.g., malate, aspartate, glutamate, ketoglutarate) and a higher level of malate dehydrogenase (MDH) in serum [11]. However, we also observed increased aspartate, glutamate and MDH. Lu et al. showed that the changed MAS during ischemia was not only due to changes in shuttle-associate enzymes, but also due to a redistribution of shuttle-associated metabolites in both cytosol and mitochondria [22]. This may explain the inconsistent results observed in our study.

Interestingly, we observed decreased levels of Aldo-keto reductase family 1 member B10 (Akr1b10), a NADPH-dependent aldo-keto reductase. Akr1b10 is primarily expressed in the human colon, small intestine and adrenal gland, and at a low level in the liver [23]. It is used as a tumor marker because it is usually overexpressed in tumors such as hepatocellular carcinoma [24]. Some researchers have shown that the mRNA for Akr1b10 was also expressed highly in rat brain and heart [25]; however no study has reported on the function of Akr1b10 in the heart. Studies have shown that Akr1b10 functions as a defense system against oxidative stress [25] and Akr1b10 silencing can result in caspase-3-mediated apoptosis [23]. Oxidative stress and apoptosis are two important pathogenesis results of MI injury. The decreased Akr1b10 in our study provides evidence for its important role in MI, but further study is needed.

We also found active amino acid metabolism was a part of the process of MI. Prolonged oxygen deficiency provokes tissue necrosis in MI, which subsequently causes protein degradation [26]. Most of the amino acids produced by protein degradation are reused to synthesize new proteins for tissue repair [27], but some of them are also used as metabolic substrates for energy provision [28]. The changed amino acids in our study are clearly shown in the ABC transporters (S4 Fig). ABC transporters are a series of ATP-dependent transport enzymes, which can transport multiple endogenous compounds including amino acids, ions, and nucleotides. Researchers have found that ABC transporters are involved in the homeostasis of the heart [29]. We found that the transport of amino acids (e.g., glutamate) was altered after MI. Glutamate is a main excitatory transmitter in the nervous system and is closely related to neurological disease [30], but other researchers have shown that glutamate may also play an important role in the regulation of non-neurological diseases, such as myocardial ischemia [31]. The oxidative deamination of glutamate, as catalyzed by glutamate dehydrogenase (GDH), is used to supply energy, which plays a significant role in the ischemic myocardium. In our study, increased glutamate in the ischemic myocardium was consistent with previous studies [31, 32]. Accordingly, we speculate that in response to energy deficiency, the ischemic myocardium spontaneously accelerates the transamination of amino acids, which leads to an increase in glutamate and a decrease in other amino acids such as taurine, arginine and alanine. In addition, we also observed decreased levels of glutamine. Glutamine, a transport form of glutamate and ammonia, can generate a large amount of glutathione (GSH), which protects the myocardium against oxidative stress injury [33]. The decreased glutamine in our study might be related to the involvement of glutamine in the repair of myocardial injury. Accordingly, we suggest that, when an MI occurs, the stress response of myocardium increases in order to compete with myocardial injury.

Previous research has demonstrated that the levels of myosin light chain 2, myosin heavy chain fragments, and heat shock proteins in the ventricles changed after reperfusion [34]. Another study reported increased angiogenesis at days 4 and 7 after reperfusion [35]. We found that myosin light polypeptide 6 (Myl6), heat shock 27kDa protein 1 (HSP27) and prostaglandin I2 (PGI2) increased and arachidonic acid (AA) decreased. AA can be synthesized into prostaglandins including PGI2 [36]. PGI2 is one of the most important vascular protectors, principally as it causes vasodilation [37]. Myl6 is a subunit of myosin and its phosphorylation in the heart can improve the contractility of cardiomyocytes [38]. Obviously, blood perfusion of ischemic myocardium may increase with enhanced myocardial contraction and vasodilation. In addition, HSP27 is expressed at a high level in the cardiovascular system and is vital to actin remodeling, which is necessary for migration of smooth muscle cells, thereby promoting the development of blood vessels [39]. Taken together, we have reason to believe that self-regulation of infarcted myocardium occurs in the early stages of MI.

Gap junction (GJs) comprised another changed pathway observed in our study. GJs can help coordinate depolarization by allowing ions to pass between cardiomyocytes in a form of electrical communication and is essential in maintaining the normal electrical activity of the heart. GJs-dependent intercellular communication relies on proper tubulin and/or tubulin network [40]. However, in our study, the expression of tubulin was altered after MI, which indicated disturbed tubulin dynamics. This provides a foundation for arrhythmia. Further, α-tubulin also shows a key role in cardiac fibrosis development and cardiac remodeling, which together have a serious impact on cardiac function [41]. Mitogen-activated protein kinase 3 (MKK3), another protein also reported to be involved in improving cardiac remodeling [42], was simultaneously decreased in our study. Evidently, detrimental cardiac remodeling has occurred in our research.

Moreover, pathway analysis showed that the altered metabolites and proteins were related to neuroactive ligand-receptor interaction. Previous research indicated that the nervous system, including the sympathetic system and the parasympathetic system, regulates cardiac activity regardless of the heart’s physiological condition [43, 44]. In addition, researchers have reported that the cardio protection effects of sevoflurane is associated with neuroactive ligand-receptor interaction in patients undergoing coronary artery bypass graft surgery [45]. Another study confirmed that neuroactive ligand-receptor interaction was closely related to human arrhythmogenic right ventricular cardiomyopathy [46]. Although the correlation of neuroactive ligand-receptor interaction in cardiovascular diseases has not been reported, this pathway may be a new mechanism in MI for future studies to investigate.

However, there are some limitations. First, a correlational analysis should be included. For our integrated analysis, we only chose KEGG pathways as the carrier and conducted a mapping analysis based on the changed proteins and metabolites; a correlational analysis can better illustrate the interaction between proteins and metabolites. Second, to further demonstrate the pathological changes after MI which we observed, more validation is warranted, specifically an additional quantification of altered proteins (such as Hsd17b10, Akr1b10, Hsp27 et al.).

Conclusions

In conclusion, this study reported a comprehensive analysis of proteomic and metabolic profiles after MI. The result indicated that myocardial injury after MI is closely related to several metabolic pathways, especially energy metabolism, amino acid metabolism, vascular smooth muscle contraction, gap junction and neuroactive ligand-receptor interaction. These findings may contribute to understanding the entire MI process, and they hold promise for the improvement of the treatment of MI.

Supporting information

S1 Fig. Peptide IonScore distribution.

More than 90.79% of the peptides scored higher than 20; the median score was 41.55. The red line indicates the cumulative curve.

https://doi.org/10.1371/journal.pone.0231797.s001

(TIF)

S2 Fig. Protein ratio distribution.

Most of the protein ratios in the MS group and the MI group were approximately 1.

https://doi.org/10.1371/journal.pone.0231797.s002

(TIF)

S3 Fig.

Total Ion Chromatograms (TIC) of myocardium metabolites under (a) positive ion mode and (b) negative ion mode. Metabolomics showed very stable performance as chromatograms were anastomotic in positive ion (n = 5) and negative ion modes (n = 6).

https://doi.org/10.1371/journal.pone.0231797.s003

(TIF)

S4 Fig. ABC transporters pathway.

Circles indicate metabolites; boxes indicate proteins. Red circles indicate increased metabolites; green circles indicate decreased metabolites. Red indicates increased proteins; dark green indicates decreased proteins.

https://doi.org/10.1371/journal.pone.0231797.s004

(TIF)

S1 Table. Metabolic pathways in which both significantly changed proteins and metabolites were involved.

https://doi.org/10.1371/journal.pone.0231797.s005

(DOCX)

References

1. Yu H, Kalogeris T, Korthuis RJ. Reactive species-induced microvascular dysfunction in ischemia/reperfusion. Free Radic Biol Med. 2019;135:182–97. pmid:30849489
- View Article
- PubMed/NCBI
- Google Scholar
2. Prabhu SD, Frangogiannis NG. The Biological Basis for Cardiac Repair After Myocardial Infarction: From Inflammation to Fibrosis. Circ Res. 2016;119(1):91–112. pmid:27340270
- View Article
- PubMed/NCBI
- Google Scholar
3. Talman V, Ruskoaho H. Cardiac fibrosis in myocardial infarction-from repair and remodeling to regeneration. Cell Tissue Res. 2016;365(3):563–81. pmid:27324127
- View Article
- PubMed/NCBI
- Google Scholar
4. Li Y, Chen B, Yang X, Zhang C, Jiao Y, Li P, et al. S100a8/a9 Signaling Causes Mitochondrial Dysfunction and Cardiomyocyte Death in Response to Ischemic/Reperfusion Injury. Circulation. 2019;140(9):751–64. pmid:31220942
- View Article
- PubMed/NCBI
- Google Scholar
5. Pouralijan Amiri M, Khoshkam M, Salek RM, Madadi R, Faghanzadeh Ganji G, Ramazani A. Metabolomics in early detection and prognosis of acute coronary syndrome. Clin Chim Acta. 2019;495:43–53. pmid:30928571
- View Article
- PubMed/NCBI
- Google Scholar
6. Cavill R, Jennen D, Kleinjans J, Briede JJ. Transcriptomic and metabolomic data integration. Brief Bioinform. 2016;17(5):891–901. pmid:26467821
- View Article
- PubMed/NCBI
- Google Scholar
7. Yoo BC, Kim KH, Woo SM, Myung JK. Clinical multi-omics strategies for the effective cancer management. J Proteomics. 2018;188:97–106. pmid:28821459
- View Article
- PubMed/NCBI
- Google Scholar
8. Zhang Y, Yuan S, Pu J, Yang L, Zhou X, Liu L, et al. Integrated Metabolomics and Proteomics Analysis of Hippocampus in a Rat Model of Depression. Neuroscience. 2018;371:207–20. pmid:29237567
- View Article
- PubMed/NCBI
- Google Scholar
9. Du C, Weng Y, Lou J, Zeng G, Liu X, Jin H, et al. Isobaric tags for relative and absolute quantitationbased proteomics reveals potential novel biomarkers for the early diagnosis of acute myocardial infarction within 3 h. Int J Mol Med. 2019;43(5):1991–2004. pmid:30896787
- View Article
- PubMed/NCBI
- Google Scholar
10. Wu Y, Liu F, Ma X, Adi D, Gai MT, Jin X, et al. iTRAQ analysis of a mouse acute myocardial infarction model reveals that vitamin D binding protein promotes cardiomyocyte apoptosis after hypoxia. Oncotarget. 2018;9(2):1969–79. pmid:29416745
- View Article
- PubMed/NCBI
- Google Scholar
11. Zhang HR, Tao JL, Bai H, Yang EM, Zhong ZH, Liu XT, et al. Changes in the Serum Metabolome of Acute Myocardial Ischemia Rat Pretreatment with Electroacupuncture. Am J Chin Med. 2019;47(5):1025–41. pmid:31327237
- View Article
- PubMed/NCBI
- Google Scholar
12. Fu SP, He SY, Xu B, Hu CJ, Lu SF, Shen WX, et al. Acupuncture promotes angiogenesis after myocardial ischemia through H3K9 acetylation regulation at VEGF gene. PloS One. 2014;9(4):e94604. pmid:24722278
- View Article
- PubMed/NCBI
- Google Scholar
13. Yin M, Sillje HH, Meissner M, van Gilst WH, de Boer RA. Early and late effects of the DPP-4 inhibitor vildagliptin in a rat model of post-myocardial infarction heart failure. Cardiovasc Diabetol. 2011;10:85. pmid:21955567
- View Article
- PubMed/NCBI
- Google Scholar
14. Schmidt EKA, Torres-Espin A, Raposo PJF, Madsen KL, Kigerl KA, Popovich PG, et al. Fecal transplant prevents gut dysbiosis and anxiety-like behaviour after spinal cord injury in rats. PloS One. 2020;15(1):e0226128. pmid:31940312
- View Article
- PubMed/NCBI
- Google Scholar
15. Wisniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359–62. pmid:19377485
- View Article
- PubMed/NCBI
- Google Scholar
16. Evans RD, Hauton D. The role of triacylglycerol in cardiac energy provision. Biochim Biophys Acta. 2016;1861(10):1481–91. pmid:26979759
- View Article
- PubMed/NCBI
- Google Scholar
17. Lopaschuk GD, Ussher JR, Folmes CD, Jaswal JS, Stanley WC. Myocardial fatty acid metabolism in health and disease. Physiol Rev. 2010;90(1):207–58. pmid:20086077
- View Article
- PubMed/NCBI
- Google Scholar
18. Surendran A, Aliani M, Ravandi A. Metabolomic characterization of myocardial ischemia-reperfusion injury in ST-segment elevation myocardial infarction patients undergoing percutaneous coronary intervention. Sci Rep. 2019;9(1):11742. pmid:31409856
- View Article
- PubMed/NCBI
- Google Scholar
19. McKirnan MD, Ichikawa Y, Zhang Z, Zemljic-Harpf AE, Fan S, Barupal DK, et al. Metabolomic analysis of serum and myocardium in compensated heart failure after myocardial infarction. Life Sci. 2019;221:212–23. pmid:30731143
- View Article
- PubMed/NCBI
- Google Scholar
20. He XY, Isaacs C, Yang SY. Roles of Mitochondrial 17beta-Hydroxysteroid Dehydrogenase Type 10 in Alzheimer's Disease. J Alzheimers Dis. 2018;62(2):665–73. pmid:29480196
- View Article
- PubMed/NCBI
- Google Scholar
21. Gray LR, Tompkins SC, Taylor EB. Regulation of pyruvate metabolism and human disease. Cell Mol Life Sci. 2014;71(14):2577–604. pmid:24363178
- View Article
- PubMed/NCBI
- Google Scholar
22. Lu M, Zhou L, Stanley WC, Cabrera ME, Saidel GM, Yu X. Role of the malate-aspartate shuttle on the metabolic response to myocardial ischemia. J Theor Biol. 2008;254(2):466–75. pmid:18603266
- View Article
- PubMed/NCBI
- Google Scholar
23. Wang C, Yan R, Luo D, Watabe K, Liao DF, Cao D. Aldo-keto reductase family 1 member B10 promotes cell survival by regulating lipid synthesis and eliminating carbonyls. J Biol Chem. 2009;284(39):26742–8. pmid:19643728
- View Article
- PubMed/NCBI
- Google Scholar
24. Ye X, Li C, Zu X, Lin M, Liu Q, Liu J, et al. A Large-Scale Multicenter Study Validates Aldo-Keto Reductase Family 1 Member B10 as a Prevalent Serum Marker for Detection of Hepatocellular Carcinoma. Hepatology. 2019;69(6):2489–501. pmid:30672601
- View Article
- PubMed/NCBI
- Google Scholar
25. Endo S, Matsunaga T, Kuragano T, Ohno S, Kitade Y, Tajima K, et al. Properties and tissue distribution of a novel aldo-keto reductase encoding in a rat gene (Akr1b10). Arch Biochem Biophys. 2010;503(2):230–7. pmid:20709016
- View Article
- PubMed/NCBI
- Google Scholar
26. Spinale FG, Janicki JS, Zile MR. Membrane-associated matrix proteolysis and heart failure. Circ Res. 2013;112(1):195–208. pmid:23287455
- View Article
- PubMed/NCBI
- Google Scholar
27. Li Z, Zhang H. Reprogramming of glucose, fatty acid and amino acid metabolism for cancer progression. Cell Mol Life Sci. 2016;73(2):377–92. pmid:26499846
- View Article
- PubMed/NCBI
- Google Scholar
28. Andersen JV, Skotte NH, Aldana BI, Norremolle A, Waagepetersen HS. Enhanced cerebral branched-chain amino acid metabolism in R6/2 mouse model of Huntington's disease. Cell Mol Life Sci. 2019;76(12):2449–61. pmid:30830240
- View Article
- PubMed/NCBI
- Google Scholar
29. Hausner EA, Elmore SA, Yang X. Overview of the Components of Cardiac Metabolism. Drug Metab Dispos. 2019;47(6):673–88. pmid:30967471
- View Article
- PubMed/NCBI
- Google Scholar
30. Burbaeva G, Boksha IS, Tereshkina EB, Savushkina OK, Prokhorova TA, Vorobyeva EA. Glutamate and GABA-metabolizing enzymes in post-mortem cerebellum in Alzheimer's disease: phosphate-activated glutaminase and glutamic acid decarboxylase. Cerebellum. 2014;13(5):607–15. pmid:24950944
- View Article
- PubMed/NCBI
- Google Scholar
31. Sun X, Zhong J, Wang D, Xu J, Su H, An C, et al. Increasing glutamate promotes ischemia-reperfusion-induced ventricular arrhythmias in rats in vivo. Pharmacology. 2014;93(1–2):4–9. pmid:24401762
- View Article
- PubMed/NCBI
- Google Scholar
32. Wang X, Wang D, Wu J, Yu X, Lv J, Kong J, et al. Metabolic Characterization of Myocardial Infarction Using GC-MS-Based Tissue Metabolomics. Int Heart J. 2017;58(3):441–6. pmid:28484125
- View Article
- PubMed/NCBI
- Google Scholar
33. Kou Y, Zheng WT, Zhang YR. Inhibition of miR-23 protects myocardial function from ischemia-reperfusion injury through restoration of glutamine metabolism. Eur Rev Med Pharmacol Sci. 2016;20(20):4286–93. pmid:27831645
- View Article
- PubMed/NCBI
- Google Scholar
34. Cadete VJ, Lin HB, Sawicka J, Wozniak M, Sawicki G. Proteomic analysis of right and left cardiac ventricles under aerobic conditions and after ischemia/reperfusion. Proteomics. 2012;12(14):2366–77. pmid:22685060
- View Article
- PubMed/NCBI
- Google Scholar
35. Binek A, Fernandez-Jimenez R, Jorge I, Camafeita E, Lopez JA, Bagwan N, et al. Proteomic footprint of myocardial ischemia/reperfusion injury: Longitudinal study of the at-risk and remote regions in the pig model. Sci Rep. 2017;7(1):12343. pmid:28955040
- View Article
- PubMed/NCBI
- Google Scholar
36. Zhou W, Zhang J, Toki S, Goleniewska K, Johnson MO, Bloodworth MH, et al. The PGI2 Analog Cicaprost Inhibits IL-33-Induced Th2 Responses, IL-2 Production, and CD25 Expression in Mouse CD4(+) T Cells. J Immunol. 2018;201(7):1936–45. pmid:30127087
- View Article
- PubMed/NCBI
- Google Scholar
37. Ling QL, Mohite AJ, Murdoch E, Akasaka H, Li QY, So SP, et al. Creating a mouse model resistant to induced ischemic stroke and cardiovascular damage. Sci Rep. 2018;8(1):1653. pmid:29374184
- View Article
- PubMed/NCBI
- Google Scholar
38. Imperatore R, Cristino L. Role of Orexin-B/Orexin 2 receptor in myocardial protection. Clin Sci (Lond). 2019;133(7):853–7.
- View Article
- Google Scholar
39. Huang J, Xie LD, Luo L, Zheng SL, Wang HJ, Xu CS. Silencing heat shock protein 27 (HSP27) inhibits the proliferation and migration of vascular smooth muscle cells in vitro. Mol Cell Biochem. 2014;390(1–2):115–21. pmid:24469469
- View Article
- PubMed/NCBI
- Google Scholar
40. Picoli C, Soleilhac E, Journet A, Barette C, Comte M, Giaume C, et al. High-Content Screening Identifies New Inhibitors of Connexin 43 Gap Junctions. Assay Drug Dev Technol. 2019;17(5):240–8. pmid:31314551
- View Article
- PubMed/NCBI
- Google Scholar
41. Tao H, Yang J-J, Shi K-H, Li J. Epigenetic factors MeCP2 and HDAC6 control α-tubulin acetylation in cardiac fibroblast proliferation and fibrosis. Inflamm Res. 2016;65(5):415–26. pmid:26975406
- View Article
- PubMed/NCBI
- Google Scholar
42. Du J, Zhang L, Wang Z, Yano N, Zhao YT, Wei L, et al. Exendin-4 induces myocardial protection through MKK3 and Akt-1 in infarcted hearts. Am J Physiol Cell Physiol. 2016;310(4):C270–83. pmid:26739490
- View Article
- PubMed/NCBI
- Google Scholar
43. Intachai K, S CC, Chattipakorn N, Shinlapawittayatorn K. Revisiting the Cardioprotective Effects of Acetylcholine Receptor Activation against Myocardial Ischemia/Reperfusion Injury. Int J Mol Sci. 2018;19(9): 2466.
- View Article
- Google Scholar
44. Ren X, Roessler AE, Lynch TLt, Haar L, Mallick F, Lui Y, et al. Cardioprotection via the skin: nociceptor-induced conditioning against cardiac MI in the NIC of time. Am J Physiol Heart Circ Physiol. 2019;316(3):H543–H553. pmid:30575436
- View Article
- PubMed/NCBI
- Google Scholar
45. Wang J, Cheng J, Zhang C, Li X. Cardioprotection Effects of Sevoflurane by Regulating the Pathway of Neuroactive Ligand-Receptor Interaction in Patients Undergoing Coronary Artery Bypass Graft Surgery. Comput Math Methods Med. 2017;2017:3618213. pmid:28348638
- View Article
- PubMed/NCBI
- Google Scholar
46. Chen P, Long B, Xu Y, Wu W, Zhang S. Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis. Front Physiol. 2018;9:1778. pmid:30574098
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Yu H, Kalogeris T, Korthuis RJ. Reactive species-induced microvascular dysfunction in ischemia/reperfusion. Free Radic Biol Med. 2019;135:182–97. pmid:30849489
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Prabhu SD, Frangogiannis NG. The Biological Basis for Cardiac Repair After Myocardial Infarction: From Inflammation to Fibrosis. Circ Res. 2016;119(1):91–112. pmid:27340270
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Talman V, Ruskoaho H. Cardiac fibrosis in myocardial infarction-from repair and remodeling to regeneration. Cell Tissue Res. 2016;365(3):563–81. pmid:27324127
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Li Y, Chen B, Yang X, Zhang C, Jiao Y, Li P, et al. S100a8/a9 Signaling Causes Mitochondrial Dysfunction and Cardiomyocyte Death in Response to Ischemic/Reperfusion Injury. Circulation. 2019;140(9):751–64. pmid:31220942
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Pouralijan Amiri M, Khoshkam M, Salek RM, Madadi R, Faghanzadeh Ganji G, Ramazani A. Metabolomics in early detection and prognosis of acute coronary syndrome. Clin Chim Acta. 2019;495:43–53. pmid:30928571
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Cavill R, Jennen D, Kleinjans J, Briede JJ. Transcriptomic and metabolomic data integration. Brief Bioinform. 2016;17(5):891–901. pmid:26467821
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Yoo BC, Kim KH, Woo SM, Myung JK. Clinical multi-omics strategies for the effective cancer management. J Proteomics. 2018;188:97–106. pmid:28821459
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Zhang Y, Yuan S, Pu J, Yang L, Zhou X, Liu L, et al. Integrated Metabolomics and Proteomics Analysis of Hippocampus in a Rat Model of Depression. Neuroscience. 2018;371:207–20. pmid:29237567
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Du C, Weng Y, Lou J, Zeng G, Liu X, Jin H, et al. Isobaric tags for relative and absolute quantitationbased proteomics reveals potential novel biomarkers for the early diagnosis of acute myocardial infarction within 3 h. Int J Mol Med. 2019;43(5):1991–2004. pmid:30896787
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Wu Y, Liu F, Ma X, Adi D, Gai MT, Jin X, et al. iTRAQ analysis of a mouse acute myocardial infarction model reveals that vitamin D binding protein promotes cardiomyocyte apoptosis after hypoxia. Oncotarget. 2018;9(2):1969–79. pmid:29416745
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Zhang HR, Tao JL, Bai H, Yang EM, Zhong ZH, Liu XT, et al. Changes in the Serum Metabolome of Acute Myocardial Ischemia Rat Pretreatment with Electroacupuncture. Am J Chin Med. 2019;47(5):1025–41. pmid:31327237
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Fu SP, He SY, Xu B, Hu CJ, Lu SF, Shen WX, et al. Acupuncture promotes angiogenesis after myocardial ischemia through H3K9 acetylation regulation at VEGF gene. PloS One. 2014;9(4):e94604. pmid:24722278
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Yin M, Sillje HH, Meissner M, van Gilst WH, de Boer RA. Early and late effects of the DPP-4 inhibitor vildagliptin in a rat model of post-myocardial infarction heart failure. Cardiovasc Diabetol. 2011;10:85. pmid:21955567
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Schmidt EKA, Torres-Espin A, Raposo PJF, Madsen KL, Kigerl KA, Popovich PG, et al. Fecal transplant prevents gut dysbiosis and anxiety-like behaviour after spinal cord injury in rats. PloS One. 2020;15(1):e0226128. pmid:31940312
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Wisniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359–62. pmid:19377485
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Evans RD, Hauton D. The role of triacylglycerol in cardiac energy provision. Biochim Biophys Acta. 2016;1861(10):1481–91. pmid:26979759
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Lopaschuk GD, Ussher JR, Folmes CD, Jaswal JS, Stanley WC. Myocardial fatty acid metabolism in health and disease. Physiol Rev. 2010;90(1):207–58. pmid:20086077
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Surendran A, Aliani M, Ravandi A. Metabolomic characterization of myocardial ischemia-reperfusion injury in ST-segment elevation myocardial infarction patients undergoing percutaneous coronary intervention. Sci Rep. 2019;9(1):11742. pmid:31409856
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. McKirnan MD, Ichikawa Y, Zhang Z, Zemljic-Harpf AE, Fan S, Barupal DK, et al. Metabolomic analysis of serum and myocardium in compensated heart failure after myocardial infarction. Life Sci. 2019;221:212–23. pmid:30731143
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. He XY, Isaacs C, Yang SY. Roles of Mitochondrial 17beta-Hydroxysteroid Dehydrogenase Type 10 in Alzheimer's Disease. J Alzheimers Dis. 2018;62(2):665–73. pmid:29480196
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Gray LR, Tompkins SC, Taylor EB. Regulation of pyruvate metabolism and human disease. Cell Mol Life Sci. 2014;71(14):2577–604. pmid:24363178
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Lu M, Zhou L, Stanley WC, Cabrera ME, Saidel GM, Yu X. Role of the malate-aspartate shuttle on the metabolic response to myocardial ischemia. J Theor Biol. 2008;254(2):466–75. pmid:18603266
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Wang C, Yan R, Luo D, Watabe K, Liao DF, Cao D. Aldo-keto reductase family 1 member B10 promotes cell survival by regulating lipid synthesis and eliminating carbonyls. J Biol Chem. 2009;284(39):26742–8. pmid:19643728
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref24] 24. Ye X, Li C, Zu X, Lin M, Liu Q, Liu J, et al. A Large-Scale Multicenter Study Validates Aldo-Keto Reductase Family 1 Member B10 as a Prevalent Serum Marker for Detection of Hepatocellular Carcinoma. Hepatology. 2019;69(6):2489–501. pmid:30672601
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref25] 25. Endo S, Matsunaga T, Kuragano T, Ohno S, Kitade Y, Tajima K, et al. Properties and tissue distribution of a novel aldo-keto reductase encoding in a rat gene (Akr1b10). Arch Biochem Biophys. 2010;503(2):230–7. pmid:20709016
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref26] 26. Spinale FG, Janicki JS, Zile MR. Membrane-associated matrix proteolysis and heart failure. Circ Res. 2013;112(1):195–208. pmid:23287455
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref27] 27. Li Z, Zhang H. Reprogramming of glucose, fatty acid and amino acid metabolism for cancer progression. Cell Mol Life Sci. 2016;73(2):377–92. pmid:26499846
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref28] 28. Andersen JV, Skotte NH, Aldana BI, Norremolle A, Waagepetersen HS. Enhanced cerebral branched-chain amino acid metabolism in R6/2 mouse model of Huntington's disease. Cell Mol Life Sci. 2019;76(12):2449–61. pmid:30830240
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref29] 29. Hausner EA, Elmore SA, Yang X. Overview of the Components of Cardiac Metabolism. Drug Metab Dispos. 2019;47(6):673–88. pmid:30967471
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref30] 30. Burbaeva G, Boksha IS, Tereshkina EB, Savushkina OK, Prokhorova TA, Vorobyeva EA. Glutamate and GABA-metabolizing enzymes in post-mortem cerebellum in Alzheimer's disease: phosphate-activated glutaminase and glutamic acid decarboxylase. Cerebellum. 2014;13(5):607–15. pmid:24950944
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref31] 31. Sun X, Zhong J, Wang D, Xu J, Su H, An C, et al. Increasing glutamate promotes ischemia-reperfusion-induced ventricular arrhythmias in rats in vivo. Pharmacology. 2014;93(1–2):4–9. pmid:24401762
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref32] 32. Wang X, Wang D, Wu J, Yu X, Lv J, Kong J, et al. Metabolic Characterization of Myocardial Infarction Using GC-MS-Based Tissue Metabolomics. Int Heart J. 2017;58(3):441–6. pmid:28484125
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref33] 33. Kou Y, Zheng WT, Zhang YR. Inhibition of miR-23 protects myocardial function from ischemia-reperfusion injury through restoration of glutamine metabolism. Eur Rev Med Pharmacol Sci. 2016;20(20):4286–93. pmid:27831645
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref34] 34. Cadete VJ, Lin HB, Sawicka J, Wozniak M, Sawicki G. Proteomic analysis of right and left cardiac ventricles under aerobic conditions and after ischemia/reperfusion. Proteomics. 2012;12(14):2366–77. pmid:22685060
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref35] 35. Binek A, Fernandez-Jimenez R, Jorge I, Camafeita E, Lopez JA, Bagwan N, et al. Proteomic footprint of myocardial ischemia/reperfusion injury: Longitudinal study of the at-risk and remote regions in the pig model. Sci Rep. 2017;7(1):12343. pmid:28955040
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref36] 36. Zhou W, Zhang J, Toki S, Goleniewska K, Johnson MO, Bloodworth MH, et al. The PGI2 Analog Cicaprost Inhibits IL-33-Induced Th2 Responses, IL-2 Production, and CD25 Expression in Mouse CD4(+) T Cells. J Immunol. 2018;201(7):1936–45. pmid:30127087
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref37] 37. Ling QL, Mohite AJ, Murdoch E, Akasaka H, Li QY, So SP, et al. Creating a mouse model resistant to induced ischemic stroke and cardiovascular damage. Sci Rep. 2018;8(1):1653. pmid:29374184
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref38] 38. Imperatore R, Cristino L. Role of Orexin-B/Orexin 2 receptor in myocardial protection. Clin Sci (Lond). 2019;133(7):853–7.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

[ref39] 39. Huang J, Xie LD, Luo L, Zheng SL, Wang HJ, Xu CS. Silencing heat shock protein 27 (HSP27) inhibits the proliferation and migration of vascular smooth muscle cells in vitro. Mol Cell Biochem. 2014;390(1–2):115–21. pmid:24469469
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref40] 40. Picoli C, Soleilhac E, Journet A, Barette C, Comte M, Giaume C, et al. High-Content Screening Identifies New Inhibitors of Connexin 43 Gap Junctions. Assay Drug Dev Technol. 2019;17(5):240–8. pmid:31314551
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref41] 41. Tao H, Yang J-J, Shi K-H, Li J. Epigenetic factors MeCP2 and HDAC6 control α-tubulin acetylation in cardiac fibroblast proliferation and fibrosis. Inflamm Res. 2016;65(5):415–26. pmid:26975406
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref42] 42. Du J, Zhang L, Wang Z, Yano N, Zhao YT, Wei L, et al. Exendin-4 induces myocardial protection through MKK3 and Akt-1 in infarcted hearts. Am J Physiol Cell Physiol. 2016;310(4):C270–83. pmid:26739490
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref43] 43. Intachai K, S CC, Chattipakorn N, Shinlapawittayatorn K. Revisiting the Cardioprotective Effects of Acetylcholine Receptor Activation against Myocardial Ischemia/Reperfusion Injury. Int J Mol Sci. 2018;19(9): 2466.
View Article
Google Scholar

[169] View Article

[170] Google Scholar

[ref44] 44. Ren X, Roessler AE, Lynch TLt, Haar L, Mallick F, Lui Y, et al. Cardioprotection via the skin: nociceptor-induced conditioning against cardiac MI in the NIC of time. Am J Physiol Heart Circ Physiol. 2019;316(3):H543–H553. pmid:30575436
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref45] 45. Wang J, Cheng J, Zhang C, Li X. Cardioprotection Effects of Sevoflurane by Regulating the Pathway of Neuroactive Ligand-Receptor Interaction in Patients Undergoing Coronary Artery Bypass Graft Surgery. Comput Math Methods Med. 2017;2017:3618213. pmid:28348638
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref46] 46. Chen P, Long B, Xu Y, Wu W, Zhang S. Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis. Front Physiol. 2018;9:1778. pmid:30574098
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

Figures

Abstract

Introduction

Methods

Experimental animals and grouping

MI model establishment

Echocardiography

Collection of tissue and serum samples

Determination of serum level of cTnT

Proteomics analysis

Metabolomics analysis

Integrated analysis of proteins and metabolites

Statistical analysis

Results

Change in left ventricular function and myocardial enzyme level after MI

Myocardial protein identification and quantification comparisons after MI

Functional classification and annotation of differentially expresses proteins

KEGG pathway analysis for differentially expressed proteins identified after MI

Metabolic profiles alterations after MI

Integrated analysis of proteins and metabolites that were altered after MI

Discussion

Conclusions

Supporting information

S1 Fig. Peptide IonScore distribution.

S2 Fig. Protein ratio distribution.

S3 Fig.

S4 Fig. ABC transporters pathway.

S1 Table. Metabolic pathways in which both significantly changed proteins and metabolites were involved.

References